Plot Neural Network Layout

I need to make a plot/graphic of my neural network. I know there’s stuff out there for plotting graphs but I can’t seem to find anything that has the typical structure with the vertices lined up and connected by edges to subsequent layers. Like so…


I’ve found a lot of generic things to make this work but I need to customize edges. Is there any method of visualizing neural networks others here are using? At this point I can only think of learning some visualization software like photoshop or exporting my Flux model to Tensorflow and using tensorboards.

TensorBoard does not display like this.

I would draw this in Tikz.


This looks perfect! Would you recommend using something like TikzGraphs.jl or just straight up Tex?

Straight Tikz in TeX.

I have a tiks tuitorial that I wrote a while back:

See also for the tikz graphing library:


An interesting exercise would be to write plot recipes for neural nets in Flux :slight_smile:

We could define a complicated network, and call plot(net) to see if we got it right.

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That should be fairly easy IMHO - if you can show me how to generate the columns and vertices I might give it a shot.
But @sdanisch has also talked about doing this for Makie, interactively, so you can see the network learn…

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@oxinabox, these resources are absolutely awesome!

@juliohm and @mkborregaard, this was the sort of thing I was originally hoping for. And having something that updates throughout training would be an incredibly useful way of monitoring a network instead of having to dig through to weights and gradients between epochs

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I feel that in general this kind of diagram is not useful.
It is good for explaining right as a very intro.

But in general this is not a good diagram for a network,
since if you ha ve 300 neurons on a layer it s unreadable.
And it encourages implmentation with neurons as objects,
rather than just matrix math.

Much more useful is a block diagram, showing layer or matrix opperations.

The exception, which I guess is what @Zach_Christensen is doing is if you have some kind of special connections, like sparse connections, or connections that have nonnegative weights.
Then it is a bit useful for showing structure

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I’m thinking of two additional ways this type of diagram would be useful throughout training.

  1. I’m working on a method that could theoretically be useful in identifying image features in the first few layers of a network. It would be great to see how much these feature contribute to a model very early on in the training process, or even while constructing a model.
  2. If I could easily visualize exploding gradients, vanishing weights, etc. it would really speed up creation of novel models.

Both of these things can be done in the code without visualization, but I think it would help my productivity and really sell people on Flux/Julia to have pretty pictures. It may seem a bit pandering at that point though.

I know it seems trivial if you actually know what you’re doing but stuff like this…

…would really help me explain what I’m doing to my colleagues in the neuroscience department and how it compares to what’s out there.

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I agree that showing all neurons is hardly useful. More useful would be to just draw a schematic like it is done in papers: a convolutional layer represented as a box of right “shape” with depth representing number of filters and so on; a fully connected layer shown as just a box with bunch of nodes…

Each of these visual representations could be standardized for the good of everyone. It is a pain to read NN papers where authors have poor drawing skills. It is often the case that something is left out in the drawing.

I would think that the most reliable way to get that information is to follow the same path of information as the gradients do. I think that’s going to be switching over to Zygote soon. Otherwise you have to find a way to get that info from each type of layer).

use this?


Thanks for sharing @yatra9, it would be nice to have something similar in Julia :slight_smile:

It is hard to beat the beauty of LaTeX + TiKZ …